award-recipient-2026 Tjeerd van der Veer

Tjeerd van der Veer

Imaging Biomarkers of Disease Progression in Alpha-1 Antitrypsin Deficiency: A Multicenter Longitudinal Automated CT Analysis (IMPACT-AATD)


Summary:

Despite advances in the management of alpha-1 antitrypsin deficiency (AATD), current monitoring still relies on lung function measurements, which do not fully capture the structural changes occurring in the lungs. Clinicians still lack reliable biomarkers to identify patients at the highest risk of disease progression. This project will apply artificial intelligence (AI)-based quantitative CT analysis to chest CT scans from AATD patients across multiple European centres to identify imaging biomarkers associated with disease severity and progression. In addition to emphysema, the study will assess airway abnormalities, mucus plugging and pulmonary vascular changes using validated automated image analysis. By combining expertise from Leiden University Medical Center, Vall d'Hebron, EARCO and the Open Source Imaging Consortium (OSIC) platform, the project aims to establish standardized multicentre imaging workflows and lay the foundation for future imaging biomarker research in AATD.

 

Biography

Dr. Tjeerd van der Veer is a pulmonologist at the Dutch Alpha-1 Antitrypsin Deficiency Expertise Centre at Leiden University Medical Center (LUMC). His research focuses on quantitative CT imaging and artificial intelligence to improve phenotyping and prognostic modeling in chronic respiratory diseases. He has worked on the development of imaging biomarkers in COPD, bronchiectasis and alpha-1 antitrypsin deficiency, with a particular focus on airway remodeling, mucus plugging, pulmonary vascular abnormalities and emphysema. He collaborates with international initiatives including EARCO, EMBARC, COPDGene and OSIC. Through the ALTA Award, he will lead an international collaboration to evaluate automated quantitative CT biomarkers in alpha-1 antitrypsin deficiency and establish standardized multicentre imaging workflows linked to clinical data.